Evidence map›Paper›PMID 38126652›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning.

Jingzhi Yang, Yami Ran, Shaopeng Liu, Chenhao Ren, Yuntian Lou, Pengfei Ju, Guoliang Li, Xiaogang Li, Dawei Zhang

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. D-amino acid enhanced the sensitivity of avian pathogenicFrontiers in veterinary science · 2025
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Jingzhi YangBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Yami RanBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Shaopeng LiuBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Chenhao RenBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Yuntian LouBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Pengfei JuShanghai Aerospace Equipment Manufacturer, Shanghai, 200245, China.
Guoliang LiCollege of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China.
Xiaogang LiBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Dawei ZhangBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.ORCID 0000-0002-6546-6181
University of Science and Technology Beijing · CNBeijing University of Chemical Technology · CNShanghai Power Equipment Research Institute · CN

Funding

Joint Fund of Basic and Applied Basic Research Fund of Guangdong Province 2021B1515130009National Natural Science Foundation of China 52071015The Beijing Nova Program 20220484224
6 · The paper itself

Abstract

Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D-amino acids mixtures coupled with antibiotics can provide new therapies for drug-resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial-and-error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high-throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D-amino acid mixtures with the highest anti-biofilm efficiency and also the synergisms between D-amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non-toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.

Indexed as

Amino AcidsAnti-Infective AgentsAnimalsAnti-Bacterial AgentsHigh-Throughput Screening AssaysMachine LearningMiceAmino AcidsAnti-Bacterial AgentsAnti-Infective Agentsantimicrobial resistanceD-amino acidshigh-throughput screeningmachine learning

Identifiers

PMID38126652
PMCPMC10916672
OpenAlexW4390051276

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.